FGITrans: Cross-City Transformer for Fine-grained Urban Flow Inference

Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. Adequate data is usually a prerequisite for existing machine learning methods, especially most deep learning models. However, many cities still suffer from the data scarcity issue due to the unbalanced city development levels. To mitigate this issue, we propose a novel cross-city fine-grained urban flow inference model named FGITrans, which aims to effectively transfer the knowledge from the data-rich cities to the data-scarce cities. Specifically, we design a weight-sharing triple-branch transformer framework which adopts self-attention and cross-attention for source/target city feature learning and domain alignment, respectively. Then, we propose a novel spatio-temporal adaptive embedding (STAE) layer for our transformer framework, and introduce a cross-city knowledge distillation (CKD) loss to narrow the cross-city disparities. The CKD loss explicitly enforces the framework to learn the discriminative domain-specific and domain-invariant representations simultaneously. Extensive experiments conducted on four large real-world datasets validate the effectiveness of FGITrans compared with the state-of-the-art baselines.